Method and system for providing user-specific explanations for output generated by artificial neural network model

ABSTRACT

This disclosure relates to method and system for providing user-specific explanations for an output generated by an Artificial Neural Network (ANN) model. The method may include receiving a training dataset, and identifying one or more relevant features from the training dataset. The method may further include distributing the one or more relevant features into a plurality of groups. The plurality of groups may correspond to a plurality of levels of domain knowledge of users. The method may further include generating a plurality of vocabularies of explanations for an output generated by the ANN model for the training dataset corresponding to the plurality of groups, using the one or more relevant features.

TECHNICAL FIELD

This disclosure relates generally to artificial neural network (ANN),and more particularly to method and system for providing user-specificexplanations for an output generated by an ANN.

BACKGROUND

Artificial Intelligence (AI) models work as black boxes which providelittle or no insights about how a decision was generated by the AImodel. As it will be appreciated by those skilled in the art, anexplanation or a resoning on “Why a certain decision is made?” would bevery useful for an end user. For example, during diagnosis of cancerfrom a plurality of Computed Tomography (CT) images, it would be helpfulif a classifier model (e.g. an AI model) provides information to theuser about generated decision in simple and plain English about. “Whatfeatures were taken into consideration to generate the decision”.Further, such information would be convincing for the user, as the samecan be referred back to the CT images for validation.

The explanations generated by the AI models have become increasinglypopular. Moreover, providing explanations is seen as a regulatoryobligation, in order to make the decision making process by the AImodels more transparent. The explainable AI models are trained based ondifferent type of users in the same domain. In an example scenario, theexplainable AI model may be trained using data associated with a set ofdoctors having different specializations. Further, the explainable AImodels may be trained based on the users corresponding to differentdomains. In another example scenario, the explainable AI models may betrained using data associated with doctors, domain experts, andnon-domain experts. In such a scenario, the explanations generated aresame for all the users, irrespective of the type of users. These kind ofexplanations may not be helpful and comprehendible for all the users. Inother words, the explanations generated by these conventional AI modelsmay not consider the domain of the user or his/her expertise inunderstanding and comprehending the technical jargons used in thegenerated explanation. For example, if the generated explanations aretechnical and filled with domain specific words, layman users may not beable to understand the explanations, although the domain experts mayeasily decode the generated explanations. On the other hand,explanations which are too general in nature (so as to satisfy a widerange of users) may create confusion and may not be able to providerequired details to a specific class of users to take an improveddecision.

Explainable AI models, for example, explainable image classificationmodels, may provide explanations, heat map, and visualization of the AImodel for an input image. Further, these AI models may generateexplanations which provide a reasoning about “why the input image wasclassified to a particular class”. However, these generated explanationsmay be difficult to understand by the non-domain users. As a result, theusers may not use these AI models at all. For example, for a classifiermodel (AI model) trained for classifying pneumonia images, theexplanations may be generated using a set of medical attributes, due towhich a non-domain user (for example, a patient) may not be able tounderstand the explanation due to presence of complicateddomain-specific words. Further, these explainable AI models may not beable to identify attributes from the images to build vocabulary (for afirst time) to provide explanations. Moreover, these explainable AImodels may not be able to identify different and matching attributes inthe activations of neurons of the AI model so as to provide explanationsfor different classes of users. Further, these explainable AI models maynot be able link wordings in the explanation to the activations of theneurons selected together with the category of the user.

SUMMARY

In one embodiment, a method of providing user-specific explanations foran output generated by an Artificial Neural Network (ANN) model isdisclosed. The method may include receiving a training dataset, andidentifying one or more relevant features from the training dataset. Themethod may further include distributing the one or more relevantfeatures into a plurality of groups. The plurality of groups maycorrespond to a plurality of levels of domain knowledge of users. Themethod may further include generating a plurality of vocabularies ofexplanations for the output generated by the ANN model for the trainingdataset corresponding to the plurality of groups, using the one or morerelevant features.

In one embodiment, a system for providing user-specific explanations foran output generated by an ANN model is disclosed. The system may includea vocabulary and explanation generating device, which may include atleast one processor and a memory communicatively coupled to the at leastone processor. The memory may store processor-executable instructions,which, on execution, may cause the processor to receive a trainingdataset, and identify one or more relevant features from the trainingdataset. The processor-executable instructions, on execution, mayfurther cause the processor to distribute the one or more relevantfeatures into a plurality of groups. The plurality of groups maycorrespond to a plurality of levels of domain knowledge of users. Theprocessor-executable instructions, on execution, may further cause theprocessor to generate a plurality of vocabularies of explanations for anoutput generated by the ANN model for the training dataset correspondingto the plurality of groups, using the one or more relevant features.

In one embodiment, a non-transitory computer-readable medium storingcomputer-executable instructions for providing user-specificexplanations for an output generated by an ANN model is disclosed. Thestored instructions, when executed by a processor, may cause theprocessor to perform operations including receiving a training dataset,identifying one or more relevant features from the training dataset,distributing the one or more relevant features into a plurality ofgroups, wherein the plurality of groups correspond to a plurality oflevels of domain knowledge of users, and generating a plurality ofvocabularies of explanations for an output generated by the ANN modelfor the training dataset corresponding to the plurality of groups, usingthe one or more relevant features.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate exemplary embodiments and, togetherwith the description, serve to explain the disclosed principles.

FIG. 1 is a block diagram of an exemplary system for providinguser-specific explanations for an output generated by an ArtificialNeural Network (ANN) model, in accordance with some embodiments of thepresent disclosure.

FIG. 2 is a functional block diagram of a system for providinguser-specific explanations for an output generated by an ANN model, inaccordance with some embodiments of the present disclosure.

FIG. 3 is a functional block diagram of a system for providinguser-specific explanations for an output generated by an ANN model, inaccordance with some alternate embodiments of the present disclosure.

FIG. 4 is a flow diagram of an exemplary process for providinguser-specific explanations for an output generated by an ANN model, inaccordance with some embodiments of the present disclosure.

FIG. 5 is a flow diagram of a detailed exemplary process for providinguser-specific explanations for an output generated by an ANN model, inaccordance with some embodiments of the present disclosure.

FIG. 6 is a Table of objects and actions detected in an image and thecorresponding explanations, in accordance with some embodiments of thepresent disclosure.

FIG. 7 is a hierarchical graph of objects and attributes, in accordancewith some embodiments of the present disclosure.

FIG. 8 is an exemplary video graph, in accordance with some embodimentsof the present disclosure.

FIG. 9 is a functional block diagram of an ANN model for generatingimage signatures, in accordance with some embodiments of the presentdisclosure.

FIG. 10 is a block diagram of an exemplary computer system forimplementing embodiments consistent with the present disclosure.

DETAILED DESCRIPTION

Exemplary embodiments are described with reference to the accompanyingdrawings. Wherever convenient, the same reference numbers are usedthroughout the drawings to refer to the same or like parts. Whileexamples and features of disclosed principles are described herein,modifications, adaptations, and other implementations are possiblewithout departing from the spirit and scope of the disclosedembodiments. It is intended that the following detailed description beconsidered as exemplary only, with the true scope and spirit beingindicated by the following claims.

Referring now to FIG. 1, an exemplary system 100 for providinguser-specific explanations for an output generated by an ANN model isillustrated, in accordance with some embodiments of the presentdisclosure. As will be appreciated, the system 100 may implement the ANNmodel for a target classification application. Further, the system mayimplement an ANN engine in order to provide user-specific explanationsfor an output generated by the ANN model. In particular, the system 100may include a vocabulary and explanation generating device 101 (forexample, server, desktop, laptop, notebook, netbook, tablet, smartphone,mobile phone, or any other computing device) that may implement the ANNengine. It should be noted that, in some embodiments, the ANN engine, byproviding user-specific explanations for an output generated by an ANNmodel, may help in understanding the reason for the decisions taken bythe ANN and, therefore, improve the classifications performed by the ANNmodel.

As will be described in greater detail in conjunction with FIGS. 2-9,the vocabulary and explanation generating device may receive a trainingdataset. The vocabulary and explanation generating device may furtheridentify one or more relevant features from the training dataset. Thevocabulary and explanation generating device may further distribute theone or more relevant features into a plurality of groups. It may benoted that the plurality of groups may correspond to a plurality oflevels of domain knowledge of users. The vocabulary and explanationgenerating device may further generate a plurality of vocabularies ofexplanations for an output generated by the ANN model for the trainingdataset corresponding to the plurality of groups, using the one or morerelevant features.

The system 100 may include a vocabulary and explanation generatingdevice 101. In some embodiments, the vocabulary and explanationgenerating device 101 may include one or more processors 102 and acomputer-readable medium (for example, a memory) 103. The system 100 mayfurther include a display 104. The system 100 may further include an ANNmodel 108 which may generate an output (e.g. a classification) for aninput data. The computer-readable storage medium 103 may storeinstructions that, when executed by the one or more processors 102,cause the one or more processors 102 to provide user-specificexplanations for the output generated by the ANN model 108, inaccordance with aspects of the present disclosure. The computer-readablestorage medium 103 may also store various data (for example, relevantfeature data, group data, group signature, captions data, correction toexplanation data, hierarchical graph, feature table, activated neurondata, and the like) that may be captured, processed, and/or required bythe system 100. The system 100 may interact with a user via a userinterface 105 accessible via the display 104. The system 100 may alsointeract with one or more external devices 106 over a communicationnetwork 107 for sending or receiving various data. The external devices106 may include, but may not be limited to, a remote server, a digitaldevice, or another computing system.

Referring now to FIG. 2, a functional block diagram of a system 200 forproviding user-specific explanations for an output generated by an ANNmodel, is illustrated, in accordance with some embodiments of thepresent disclosure. The system 200 may include a feature generatingmodule 201, a group feature identifying module 202, a classifier module203, a user-specific explanation module 204, a vocabulary correctionmodule 205, and a data repository 206.

The feature generating module 201 may receive training dataset 207. Insome embodiments, the training dataset 207 may include an image whichmay be used for training the user-specific explanation module 204. Insome embodiments, the image may be cropped to 28>28 and provided asinput to the feature generating module 201 for extracting the featuresand associated objects. The image may be provided as input to the groupfeature identifying module 202 and the classifier module 203 forgenerating image signatures. It may be noted that the classifier module203 may also provide the class information (output) of the image.

After receiving the training dataset 207, the feature generating module201 may extract one or more features from the training dataset (i.e. allthe images used in a classifier model). In some embodiments, the one ormore features may include different objects in the image (i.e. trainingdataset), their interactions, actions, and attributes. For example, theattributes may include a color, a shape, a size, a position, aninteraction with other objects and surroundings, and a form the objectcan take (for example, for a blocked coronary artery (object), anattribute may include a percentage of blockage).

In some embodiments, a vocabulary of explanations may be derived byparsing one or more captions associated with the image, or by usingwords which are provided by a domain expert. It may be noted that thecaptions may be generated based on the objects and their interactions inthe scene. By way of another example, the feature generating module 201may further include a caption generator (not shown in FIG. 2). Thecaption generator (not shown in FIG. 2) may be trained with differentobjects and their interactions. For example, conventional captiongenerators, such as Common Objects in Context (COCO) may be used togenerate the one or more captions. In some embodiments, the featuregenerating module 201 may include a Natural Language Processing (NLP)engine which may generate the attributes. Once the captions aregenerated, the captions may be fed to the NLP engine which may extractat least one of named entities, verbs, adverbs, and adjectives. It maybe noted that this extracted information may form the attributes of theobjects.

The group feature identifying module 202 may receive the trainingdataset (image) 207. It may be understood that the training dataset 207may indicate the type of images which may be used as input. The groupfeature identifying module 202 may not perform any processing on thereceived image. The group feature identifying module 202 may identifycommonly encountered objects in the received image. The group featureidentifying module 202 may identify common objects in the image atvarying levels of abstraction. Further, the group feature identifyingmodule 202 may be configured to receive the features and attributes fromthe feature generating module 201, and segregate the attributes that arespecific to a group.

As it will be appreciated by those skilled in the art, a differentexplanation for the output (from the classifier module 203) may beprovided to different user groups, based on a level of expertise ofusers of each user group, a familiarity of the vocabulary, and usage ofthe explanation. In some embodiments, a hierarchy of attributes may beformed. For example, for a coronary angio image, the hierarchy ofattributes may be as shown below:

-   -   Heart>blood vessels and chambers>blocked blood vessels

The hierarchy of attributes may map to hierarchy of expertise of theusers (of different user groups). In some embodiments, the hierarchy ofexpertise of the users may be specified at the time of training. Forexample, a hierarchy of expertise of the users may be as shown below:

-   -   Patient>medical attendant>Radiologist

It may be noted that different user groups may be organizedhierarchically into multiple levels, based on domain knowledge which therespective users need to comprehend and use the explanation. Theattributes may be mapped to these multiple levels. The hierarchy ofattributes may be mapped to the different users at the time of training,by a domain expert. The users may be provided with password or groupsignatures 208. The group signatures 208 may eventually map to thedifferent levels of hierarchy of the attributes. In some embodiments,sample records of the explanation for each group may be used. Inalternate embodiment, the mapping may be performed heuristically (i.e.the levels may be mapped to hierarchy of attributes). It may be notedthat the system may learn the vocabularies of explanations(corresponding to different user groups) and attributes with the rightmapping adaptively in the vocabulary correction module 205. In someembodiment, the different groups may have one or more attributes incommon. In some embodiments, the group feature identifying module 202may generate a feature table including the attributes. The group featureidentifying module 202 may send the feature table to the data repository206 for storing the same.

The data repository 206 may receive the feature table from the groupfeature identifying module 202. In some embodiments, the data repository206 may be a fast-accessible storage that may store intermediateresults. The data repository 206 may be accessed by the user-specificexplanation module 204 for referring back to the attributes. It may benoted that the feature table may be made available for the user-specificexplanation model 204 for decision-making.

The classifier module 203 may receive the training dataset (image) 207.After receiving the training dataset 207, the classifier module 203 maygenerate image signatures. In some embodiments, the classifier module203 may use a Convolutional Neural Network (CNN) for generating theimage signatures. In alternate embodiments, the classifier module 203may use auto-encoders for generating the image signatures.

The user-specific explanation module 204 may be configured to receivethe feature table from the data repository 206 and the image signaturesfrom the classifier module 203. The user-specific explanation module 204may be configured to receive the group signature 208. It may be notedthat the group signature 208 may indicate a type of user for which theexplanations need to be generated at multiple hierarchical levels. Insome embodiments, the user may provide the group signatures 208.

In some embodiments, the user-specific explanation module 204 mayreceive the features of the image at the output of a penultimate layerof the ANN model (for example, the CNN based classifier module 203),concatenated with the group signatures (for example, a password) 208.For the purpose of training, the training dataset 207 and theircorresponding attributes (for example, a class of the classifier moduleand group of the user) may be received from the data repository 206.

Then the user-specific explanation module 204 may generate explanations209 from the attributes using a Long Short Term Memory (LSTM) model. Theexplanations 209 may be generated word by word. It may be further notedthat the next word of the sequence may be generated based on theconcatenated data and the current word generated by the system. The LSTMmodel may generate probabilities for different words in the vocabularyto be the next word. A word with the highest probability is selected asthe next word. Based on the group signatures 208, distinct explanationsmay be generated for each of the training image. In other words,multiple explanations for an output generated by the ANN model may begenerated for each image corresponding to the different user groups.

The vocabulary correction module 205 may receive the generatedexplanations 209 from the user-specific explanation module 204, and mayhelp in fine-tuning the explanations 209. In another embodiment, thevocabulary correction module 205 may help in obtaining right activation(i.e. signatures) as an input to the user-specific explanation module204. Further, the vocabulary correction module 205 may receive thecorrections to be made to the explanations 209 from the user through aUser Interface (UI) based on if the word is from the correct vocabularyspecific to the class or if it is from the vocabulary of other class. Itmay be noted that even in the case a word is identified in the rightclass, the word should be from the vocabulary maintained for the rightclass of the user. The vocabulary correction module 205 may send thefine-tuning parameters to the user-specific explanation module 204. Thevocabulary correction module 205 may capture any outlandish vocabularyfor a user-group and may eliminate the same in subsequent explanations.

Referring now to FIG. 3, a functional block diagram of a system 200 forproviding user-specific explanations for an output generated by an ANNmodel during test phase (using test dataset), is illustrated, inaccordance with some embodiments of the present disclosure. The system200 may include the classifier module 203, the user-specific explanationmodule 204, the vocabulary correction module 205, and the datarepository 206.

In some embodiments, the classifier module 203 may receive a testdataset 301. In some embodiments, test dataset 301 may be an image fileor a video file. Upon receiving the test dataset 301, the classifiermodule 203 may generate image signatures or password. The test dataset301 may be typically a 28×28 size image or cropped to that size. It maybe understood that the system 200 may generate user-specificexplanations 209 for an output generated for the test dataset 301 by theclassifier module 203. It may be further understood that the generatedexplanations 209 may indicate the class to which the image belongs inaddition to the reason for classifying the image into that specificclass.

The user-specific explanation module 204 may receive the test dataset301. The user-specific explanation module 204 may further receive imagesignature from the classifier module 203. Furthermore, the user-specificexplanation module 204 may receive group signature (i.e. group identity)208 from a user. It may be noted that the group signature 208 mayindicate a user group for which the explanation is sought. In someembodiments, the group signature 208 may be provided to the user in theform of password or login credential that identify the user group.

The user-specific explanation module 204 may further receive the featuretable (also called attribute table) from the data repository 206. Theuser-specific explanation module 204 may be configured to generateexplanations 209 for the classification of the test dataset 301.Further, user-specific explanation module 204 may receive imagesignature and user-group identity for which a customized explanation isto be generated.

In some embodiments, the data repository 206 may store connecting wordsof the language, such as prepositions, articles, conjunctions, etc.,which may provide vocabulary to be used while generating theexplanations for a user-group. The vocabulary may be stored as look-uptable for the probability of next word generated by the LSTM in theuser-specific explanation module 204. It may be noted that the imagefeatures tapped from the penultimate layer of the classifier module 203may bind to the vocabulary specific to the group through the LSTM modelto generate human readable sentences.

The user-specific explanation module 204 may further receive thefeatures of the image being classified and concatenated with therequired signature (thereby, identifying a class of the user). Theuser-specific explanation module 204 may include a LSTM module which maybe configured to generate the explanation in the form of text inreal-time.

The data repository 206 may store the vocabulary and feature tablespecific to the indicated group of the user. In some embodiments, thedata repository 206 may include a radius server.

The vocabulary correction module 205 may receive the explanations 209from the user-specific explanation module 204. In some embodiments, thevocabulary correction module 205 may help in updating the vocabularyspecific to group of users and for a specific class of the classifieroutput, so as to support adaptive and continuous learning from the userinput. It may be noted that the vocabulary binding with different imagefeatures may be fine-tuned by the vocabulary correction module 205,especially, during the training phase.

Referring now to FIG. 4, an exemplary control logic 400 for providinguser-specific explanations for an output generated by an ANN model isdepicted via a flowchart, in accordance with some embodiments of thepresent disclosure.

At step 401, a training dataset 207 may be received. In someembodiments, the training dataset 207 may include an image file or avideo file. At step 402 one or more relevant features may be identifiedfrom the training dataset 207. In some embodiments, the identifying mayfurther include a step 411. It may be noted that the one or morerelevant features may include at least one of one or more objects withinthe training dataset 207, an interaction between two or more objectswithin the training dataset 207, an action associated with an object, orone or more attributes associated with each of the one or more objects,wherein the one or more attributes comprise at least one of a color ofthe object, a shape of the object, a size of the object, a position ofthe object, or an interaction of the object with other objects andsurroundings. In some embodiments, the one or more relevant features maybe identified from a plurality of features associated with the trainingdataset 207. Further, each of the plurality of features may correspondto a neuron activated in the ANN model for generating an output for thetraining dataset 207.

At step 411, one or more relevant features may be selected from theplurality of features associated with the training dataset 207, based ona predetermined threshold level of activation of the neuroncorresponding to the each of the plurality of features. At step 403, theone or more relevant features may be distributed into a plurality ofgroups. It may be noted that the plurality of groups may correspond to aplurality of levels of domain knowledge of users.

In some embodiments, the control logic 400 may include an additionalstep of 404 at which, upon distributing the one or more relevantfeatures into a plurality of groups, the one or more relevant featuresmay be arranged and stored in form of a hierarchical graph. Thehierarchical graph may represent the plurality of levels of domainknowledge of the users.

At step 405, a plurality of vocabularies of explanations may begenerated for an output generated by the ANN model for the trainingdataset 207 corresponding to the plurality of groups, using the one ormore relevant features. In some embodiments, generating the plurality ofvocabularies of explanations may further include steps 412-416. Forexample, at step 412, one or more captions corresponding to each of theone or more relevant features may be received. It may be noted that theone or more captions may be generated using Common Objects in Context(COCO). It may be further noted that one or more attributes associatedwith each of the one or more relevant features may be generated from theone or more captions using Natural Language Processing (NLP). At step413, a dataset signature corresponding to the training dataset 207 maybe received from the ANN model. At step 414, a group signature 208corresponding to each of the plurality of groups may be received. Atstep 415, a feature table may be received. The feature table may includeconnecting words of a language. At step 416, a vocabulary may begenerated for a group of the plurality of groups based on the datasetsignature, the group signature 208, and the feature table. In someembodiments, generating the plurality of vocabularies may furtherinclude feeding the one or more relevant features of the trainingdataset 207 at an output of a penultimate layer of the ANN model.

At step 406, a plurality of explanations may be generated for the outputgenerated by the ANN model for the training dataset 207 corresponding tothe plurality of groups, using the plurality of vocabularies ofexplanations. The plurality of explanations may be generated from theplurality of vocabularies of explanations using LSTM model.

At step 407, a correction to an explanation 209 of the plurality ofexplanations may be received from a user. In some embodiments, thecorrection to the explanation 209 may be based on determining by theuser that a word in the explanation 209 corresponding to a group of theplurality of groups is from a vocabulary of explanation corresponding tothe group. In alternate embodiments, the correction to the explanation209 may be based on determining by the user that a word in theexplanation 209 corresponding to a group is from a correct vocabulary ofexplanation 209 corresponding to the group.

It may be understood that the above steps may be performed duringtraining of the system for providing user-specific explanations for anoutput generated by an ANN model. Once the system is trained, the systemmay be used during test phase (for test dataset) for providinguser-specific explanations for an output generated by an ANN model.Accordingly, at step 408, a test dataset 301 may be received. At step409, a target group signature corresponding to a target group may bereceived from the plurality of groups. At step 410, a vocabulary ofexplanation for the output generated by the ANN model for the testdataset 301 may be selected from the plurality of vocabularies ofexplanations, based on the target group signature.

Referring now to FIG. 5, an exemplary control logic 500 for providinguser-specific explanations for an output generated by an ANN model isdepicted in greater detail via a flowchart, in accordance with someembodiments of the present disclosure. Steps 501-510 of the controllogic 500 may be performed in offline mode, or in other words, duringtraining of the ANN model. Steps of 511-514 of the control logic 500 maybe performed in online mode, or in other words, during testing of theANN model. As illustrated in the flowchart, at step 501 one or moretraining images may be received from an image data repository. Forexample, the one or more training images may be received by the featuregenerating module 201. It may be understood that the one or moretraining images may be used for training the user-specific explanationmodule 204. In some embodiments, each of the one or more training imagesmay be cropped to 28×28.

At step 502, one or more common features may be extracted from thetraining image. It may be noted that the one or more common features mayinclude at least one of associated objects available in the image,interactions between the objects, actions performed by the objects, andattributes (descriptors) associated with each of the object. It may befurther noted that the attributes (descriptors) may be used to generatevocabulary for explanations for an output generated by the ANN model.

In some embodiments, the feature generating module 201 may receive thetraining image (input image). After receiving the training images, thefeature generating module 201 may extract the features from the trainingdata 207 (i.e., all the training images used in the classifier module203 development and corresponding explanations). It may be noted thatthe attributes may correspond to color, shape, size, position,interaction (with other objects), action, environment, forms it takes(for example, in a blocked coronary artery, the attributes may include apercentage of blockage).

In some embodiments, at step 502, additionally, features contributingfor a desired explanation may be identified. These features contributingfor the desired explanation may be identified based on the trainingdataset 207. The features identified in most of the training images ofthe training dataset 207 corresponding to a specific class may beincluded into the vocabulary for explanation. For example, featuresidentified in a threshold minimum number of training images (e.g. athreshold of 90%) may be included into the vocabulary for explanation.In some embodiments, for identifying the features (for example, actions,interactions, objects, and the attributes), one or more conventionalmechanisms including SURF, SIFT, HAAR feature and SVM, Random Forest,Decision Tree, and Neural Network may be used. For example, the featuregenerating module 201 may identify the features from each training imageof the training dataset 207.

In some embodiments, maximum activation neurons in the ANN model (i.e.the classifier module 203) may be identified, for an output generated bythe ANN model for an input image. By way of an example, all the neuronsthat are activated for an entity may be considered. In another example,only a fraction of neurons or a fixed number of neurons (for example, 6neurons) may be considered. In some embodiments, activation level of theneurons may be arranged in a descending order, and neurons within 50% ofthe maximum activation may be considered. Further, the neurons of eachlayer may be identified and entered into a Table with row and columnaddress. An exemplary Table is shown in FIG. 6.

Referring now to FIG. 6, a Table 600 of objects and actions detected inan image and the corresponding explanations is illustrated, inaccordance with an embodiment. In the Table 600, column 601 includesentities in the image, column 602 includes entity types of therespective entities, column 603 includes image segments (or videosegment) of the image which shows the respective entities, and column604 includes the maximum neurons which are active in each layer, for therespective entities. For example, as shown in the FIG. 6, the entitieslisted in the column 601 may include a ‘ball’, a ‘hitting ball with abat’, a ‘ball moving to goal’, and a ‘big red ball’, and entity types(of the entities of Column 601) listed in the Column 602 may include an‘object’, an ‘interaction’, an ‘action’, and an ‘attribute’. Forexample, for the entity ‘ball’ of the entity type ‘object’ in an imagesegment ‘Ball1_jpg’ of an image, neuron (3,4) and (2,3) of layer L1 ofthe ANN model activated. In addition, one or more neurons of the layerL2 may be activated. In some embodiments, for a given image (or a video)input, the objects may be stored in a hierarchical graph, as explainedin greater detail in conjunction with FIG. 7.

Referring now to FIG. 7, a hierarchical graph 700 of objects andattributes is illustrated, in accordance with an embodiment. A column701 of the hierarchical graph 700 includes attributes associated withrespective objects in a column 702. Further, a column 702 includesrespective vocabularies associated with the different user-groups. Thevocabulary may be derived from the training dataset 207 provided by adomain expert or from the parsed words of captions of the image (orvideo). For example, for a user-group ‘Patient’, the associated objectand attribute may include ‘heart’ and ‘large’, respectively. Forexample, for a user-group ‘Student, the associated object and attributemay include ‘blood-vessels’ and ‘thin, respectively. Further, forexample, for a user-group ‘Doctor, the associated object and attributemay include ‘coronary-artery’, and ‘blocked’, respectively. Theexplanation 209 of an output generated for the above image may begenerated using the vocabularies, for each user-group. For example, forthe user-group, ‘Patient’, the explanation 209 may be as below:

It is abnormal because the heart is large

Similarly, for the user-group ‘Student’, the explanation 209 may be asbelow:

It is abnormal because the blood-vessel is thin.

In some embodiments, possible captions and descriptors may be includedin a Table (like Table 600) as sub-entries. For instance, in the aboveexample, for an input image of a static X-ray of a heart with a coronaryartery having a portion blocked, the object descriptors may include theheart, one or more blood vessels, the coronary artery, and the blockedportion of the coronary artery.

It may be noted that the explanations 209 may be generated in one ormore languages, corresponding to each of the one or more user-groups.Referring now to FIG. 8, an exemplary video graph 800 is illustrated, inaccordance with an example. The video graph 800 stores actions andinteractions along with the objects. For example, the objects include‘Plaster of Paris’ 801, ‘3-d Printer’ 802, and ‘Eiffel Tower Model’ 803.The interactions include ‘Poured’ 804 and ‘Generated’ 805. Theexplanations for the video graph 800 may be generated in two languages,as shown below:

English:

-   -   Plaster of Paris poured to 3-d printer.    -   The 3-d printer generated Eiffel Tower Model.

French:

-   -   Plâtre de Paris coulé sur une imprimante 3D.    -   Le modelé de tour Eiffel géneré par.

Returning back to FIG. 5, at step 503, one or more training images maybe received as input along with extracted features including theattributes. For example, the group feature identifying module 202 mayreceive the one or more training images. The group feature identifyingmodule 202 may further receive the extracted features including theattributes from the feature generating module 201. In some embodiments,object specific data may be transferred from the feature generatingmodule 201 to the group feature identifying module 202 for furtherprocessing.

At step 504, the attributes may be segregated by the group featureidentifying module 202, to generate one or more user specific groups.The generated user specific groups may be organized into multiple levelshierarchically based on domain knowledge that the users of that groupneed to have to comprehend and use the explanation. Further, at step504, a set of features that get into the vocabulary of explanation for aspecific group of audience may be identified.

By way of an example, an image (or a video segment) and correspondingentity interested in the specific group may be determined. It may beunderstood that although the maximum depth of hierarchy in a hierarchygraph may indicate maximum possible groups (and explanationscorresponding to the images making use of these vocabularies), it can bemade less than that as well. For example, if there are 4 levels in ahierarchy, Level-1 can be in Category I, Level-2 and Level-3 can be inCategory II, and Level-4 can be in Category III. Alternately, ahierarchy may fall in more than one category. These parameters may beconfigurable, and may be mapped to the set of users, for example, theset of users may be {patient, student, clinical expert}. For example,for an angiogram image input, an explanation for a ‘clinical expert’user group may include “left coronary artery”, while an explanation fora ‘patient’ user group, who is not familiar with medical terminologies,may include “Artery”, although they refer to same image features forexplanation. The classifier module 203 may be injected extensively withimages of these features to generate signatures that may include desiredfeatures. The output of a LSTM model may be represented in a graphicalform.

At step 505, a feature table may be generated by the group featureidentifying module 202. The feature table may include attributes mappedto multiple levels of user groups. The feature table generated from theattributes of the image in hierarchical fashion (reflecting differentuser groups) may be stored in the data repository 206. It may be notedthat these attributes may form the vocabulary for the explanation fordifferent group of users. At step 506, the user-specific explanationmodule 204 may receive the feature table from the data repository 206,the image signatures from the classifier module 203, and groupsignatures 208 from a user.

At step 507, explanations 209 may be generated by the user-specificexplanation module 204. The explanations 209 may be generated word byword in a sequence, based on the concatenated received data and thecurrent word generated by the system. Further, at step 507, theclassifier module 203 may be trained. An explanation model may bedeveloped to generate the explanations 209 corresponding to the usergroups. It may be understood that the explanation model may be trainedto cater for more than one group of users. The user categories may befed as external input to the model at the time of generating explanation209 unique to the category. Essentially, the group signature 208 may betreated as extended feature for the selection of right vocabulary. Byway of an example, the explanation model may include LSTM. The LSTM maybe fed with the signatures of the image to be explained along withidentity of the group to distinguish the explanations of differentgroups. It may be further understood that a group may be represented bya unique pattern and concatenated with penultimate layer output of theANN model. The ANN model is further explained in detail, in conjunctionwith FIG. 9. Here, training also considers this auxiliary pattern togenerate the explanation 209.

Referring now to FIG. 9, an ANN model 900 for generating imagesignatures is illustrated, in accordance with an embodiment. The ANNmodel 900 may include various layers 901-904. The ANN model 900 may befed with an input image 905 for which the ANN model 900 may generate anoutput in form of image class 906. In some embodiments, image signatures907 may be received after the penultimate layer 903. The training of theANN model may consider an auxiliary pattern to generate the explanation.In some embodiments, activations of penultimate layer may form a fixedpattern for different classes. This output may be truncated so that onlythose features contributing for explanation for a specific category areretained. For example, a word like ‘artery’ may not be required and aword ‘heart’ may be sufficient in some cases.

Returning back to FIG. 5, at step 508, the generated explanations andcorrections to be made to the explanations 209 may be received from theuser. For example, the generated explanations 209 may be received by thevocabulary correction module 205 from the user-specific explanationmodule 204. At step 509, fine-tuning parameters may be sent by thevocabulary correction module 205 to the user-specific explanation module204 for updating the generated explanations 209. It may be noted thatthe corrections to the explanations 209 may happen during a pilotperiod, through the user interaction. In some embodiments, if the useragrees to the explanation 209, a credit gets incremented by an amountequal to the number of correct words for the class. On the other hand,if the word of a different class is found in the explanation 209 (i.e.the user disagrees), the credit may be decremented, for example, byindicating a heavy penalty. If the user finds some explanation 209 isnot matching with the input entity, the entity may be marked throughuser interface (UI). Similarly, appearance of additional words may beindicated over the UI. These words may then be mapped to the neurons ofthe ANN model using the feature, for example, Table 600. A discrepancyin the activation may be noted. To obtain the right choice of words,weights of the ANN model may be tuned to generate required targetactivations. A current activation may be mapped to the target activationthrough a non-linear transformation. In an embodiment, a MultilayerPerceptron may be used. Thereafter, the classifier model weights may berestored back.

At step 510, an input image and image signature corresponding to theinput image may be received by the user-specific explanation module 204from the classifier module 203, and the group signature 208 may bereceived from the user. At step 511, the explanations 209 may begenerated by the user-specific explanation module 204. The explanations209 may be generated word by word in a sequence based on theconcatenated data obtained by combining the input image, the imagesignature, and the group signature 208. Further, at step 511, selectiveexplanation 209 may be generated for a given image or video when thesystem is deployed. A right signature of the user-group may be appliedto get the explanation 209 applicable to the specific class of user.This ensures authenticity to get the right explanation. In someembodiments, a password, for example, in form of an auxiliary pattern,may be provided to a specific user group. In some embodiments, theexplanations 209 may be personalized to a specific user using his/herchoice of words. In case the password is not matching, the explanation209 may contain random words. The generated explanations 209 may be sentby the user-specific explanation module 204 to the vocabulary correctionmodule 205. The vocabulary correction module 205 may receive theexplanation 209 to look for the correctness of the words based on itsrelevance or fitness for the class. Thereafter, the user-specificexplanation module 204 may receive the fine-tuning parameters from thevocabulary correction module 205. Accordingly, the relevance ofdifferent words in the explanation 209 may be received by theuser-specific explanation module 204.

At step 512, the explanations 209 may be updated by the user-specificexplanation module 204 based on the received fine-tuning parameters. Thefine-tuning parameters may be in the form of varied probabilities fordifferent words (that may indicate new words to be included or existingwords to be excluded from a class vocabulary). The fine-tuningparameters may be provided to the explanation model which in turn mayupdate the fine-tuning parameters in the vocabularies in theuser-specific explanation module 204.

At step 513, the explanations 209 may be rendered by the user-specificexplanation module 204 to the user. The explanation model may render areason behind the classification based on the user class. The user classmay be specified as password or signature unique to the group or theperson. In some embodiments, the explanation 209 may be in the form ofcomprehendible text. In some embodiments, the explanation 209 mayinclude simple figures.

Use Case 1:

For example, a hospital uses a decision support system to provide secondopinion on the presence or absence of breast cancer from a ComputedTomography (CT) image, where the system provides detailed report offindings and the impression adequate for a radiologist to accept.However, the report may be comprehensive, but not detailed enough for astudent of radiology to pursue as case study. Further, the report maynot be in plain English to be understood by the patient. The hospitalmay implement the application as provided by the proposed inventionwhich may provide distinct reports of the same information aboutrequired details, based on the needs of the user.

Use Case 2:

For example, organizers of a football match may use a robot powered byArtificial Intelligence (AI) to provide live commentary of the match.The commentary may be in English and subsequently translated to otherlanguages with a delay of a few seconds. However, the delay and thequality of translation may not be acceptable to the audience. In such ascenario, the organizers may implement the application using theproposed invention which may be able to generate sentences (commentary)without delay and in multiple languages, making use of vocabularies andtraining models of multiple languages.

As will be also appreciated, the above described techniques may take theform of computer or controller implemented processes and apparatuses forpracticing those processes. The disclosure can also be embodied in theform of computer program code containing instructions embodied intangible media, such as floppy diskettes, solid state drives, CD-ROMs,hard drives, or any other computer-readable storage medium, wherein,when the computer program code is loaded into and executed by a computeror controller, the computer becomes an apparatus for practicing theinvention. The disclosure may also be embodied in the form of computerprogram code or signal, for example, whether stored in a storage medium,loaded into and/or executed by a computer or controller, or transmittedover some transmission medium, such as over electrical wiring orcabling, through fiber optics, or via electromagnetic radiation,wherein, when the computer program code is loaded into and executed by acomputer, the computer becomes an apparatus for practicing theinvention. When implemented on a general-purpose microprocessor, thecomputer program code segments configure the microprocessor to createspecific logic circuits.

The disclosed methods and systems may be implemented on a conventionalor a general-purpose computer system, such as a personal computer (PC)or server computer. Referring now to FIG. 10, a block diagram of anexemplary computer system 1001 for implementing embodiments consistentwith the present disclosure is illustrated. Variations of computersystem 1001 may be used for implementing system 100 for providinguser-specific explanations for an output generated by an ANN. Computersystem 1001 may include a central processing unit (“CPU” or “processor”)1002. Processor 1002 may include at least one data processor forexecuting program components for executing user-generated orsystem-generated requests. A user may include a person, a person using adevice such as those included in this disclosure, or such a deviceitself. The processor 1002 may include specialized processing units suchas integrated system (bus) controllers, memory management control units,floating point units, graphics processing units, digital signalprocessing units, etc. The processor 1002 may include a microprocessor,such as AMD® ATHLON®, DURON® OR OPTERON®, ARM's application, embedded orsecure processors, IBM® POWERPC®, INTEL® CORE® processor, ITANIUM®processor, XEON® processor, CELERON® processor or other line ofprocessors, etc. The processor 1002 may be implemented using mainframe,distributed processor, multi-core, parallel, grid, or otherarchitectures. Some embodiments may utilize embedded technologies likeapplication-specific integrated circuits (ASICs), digital signalprocessors (DSPs), Field Programmable Gate Arrays (FPGAs), etc.

Processor 1002 may be disposed in communication with one or moreinput/output (I/O) devices via I/O interface 1003. The I/O interface1003 may employ communication protocols/methods such as, withoutlimitation, audio, analog, digital, monoaural, RCA, stereo, IEEE-1394,near field communication (NFC), FireWire, Camera Link®, GigE, serialbus, universal serial bus (USB), infrared, PS/2, BNC, coaxial,component, composite, digital visual interface (DVI), high-definitionmultimedia interface (HDMI), radio frequency (RF) antennas, S-Video,video graphics array (VGA), IEEE 802.n/b/g/n/x, Bluetooth, cellular(e.g., code-division multiple access (CDMA), high-speed packet access(HSPA+), global system for mobile communications (GSM), long-termevolution (LTE), WiMAX, or the like), etc.

Using the I/O interface 1003, the computer system 1001 may communicatewith one or more I/O devices. For example, the input device 1004 may bean antenna, keyboard, mouse, joystick, (infrared) remote control,camera, card reader, fax machine, dongle, biometric reader, microphone,touch screen, touchpad, trackball, sensor (e.g., accelerometer, lightsensor, GPS, altimeter, gyroscope, proximity sensor, or the like),stylus, scanner, storage device, transceiver, video device/source,visors, etc. Output device 1005 may be a printer, fax machine, videodisplay (e.g., cathode ray tube (CRT), liquid crystal display (LCD),light-emitting diode (LED), plasma, or the like), audio speaker, etc. Insome embodiments, a transceiver 1006 may be disposed in connection withthe processor 1002. The transceiver 1006 may facilitate various types ofwireless transmission or reception. For example, the transceiver 1006may include an antenna operatively connected to a transceiver chip(e.g., TEXAS INSTRUMENTS® WILINK WL1286®, BROADCOM® BCM4550IUB8®,INFINEON TECHNOLOGIES® X-GOLD 618-PMB9800® transceiver, or the like),providing IEEE 802.11a/b/g/n, Bluetooth, FM, global positioning system(GPS), 2G/3G HSDPA/HSUPA communications, etc.

In some embodiments, the processor 1002 may be disposed in communicationwith a communication network 1008 via a network interface 1007. Thenetwork interface 1007 may communicate with the communication network1008. The network interface 1007 may employ connection protocolsincluding, without limitation, direct connect, Ethernet (e.g., twistedpair 10/100/1000 Base T), transmission control protocol/internetprotocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. Thecommunication network 1008 may include, without limitation, a directinterconnection, local area network (LAN), wide area network (WAN),wireless network (e.g., using Wireless Application Protocol), theInternet, etc. Using the network interface 1007 and the communicationnetwork 1008, the computer system 1001 may communicate with devices1009, 1010, and 1011. These devices 1009, 1010, and 1011 may include,without limitation, personal computer(s), server(s), fax machines,printers, scanners, various mobile devices such as cellular telephones,smartphones (e.g., APPLE® IPHONE®, BLACKBERRY® smartphone, ANDROID®based phones, etc.), tablet computers, eBook readers (AMAZON® KINDLE®,NOOK® etc.), laptop computers, notebooks, gaming consoles (MICROSOFT®XBOX®, NINTENDO® DS®, SONY® PLAYSTATION®, etc.), or the like. In someembodiments, the computer system 1001 may itself embody one or more ofthese devices 1009, 1010, and 1011.

In some embodiments, the processor 1002 may be disposed in communicationwith one or more memory devices 1015 (e.g., RAM 1013, ROM 1014, etc.)via a storage interface 1012. The storage interface 1012 may connect tomemory devices 1015 including, without limitation, memory drives,removable disc drives, etc., employing connection protocols such asserial advanced technology attachment (SATA), integrated driveelectronics (IDE), IEEE-1394, universal serial bus (USB), fiber channel,small computer systems interface (SCSI), STD Bus, RS-232, RS-422,RS-485, I2C, SPI, Microwire, 1-Wire, IEEE 1284, Intel®QuickPathInterconnect, InfiniBand, PCIe, etc. The memory drives mayfurther include a drum, magnetic disc drive, magneto-optical drive,optical drive, redundant array of independent discs (RAID), solid-statememory devices, solid-state drives, etc.

The memory devices 1015 may store a collection of program or databasecomponents, including, without limitation, an operating system 1016,user interface application 1017, web browser 1018, mail server 1019,mail client 1020, user/application data 1021 (e.g., any data variablesor data records discussed in this disclosure), etc. The operating system1016 may facilitate resource management and operation of the computersystem 1001. Examples of operating systems 1016 include, withoutlimitation, APPLE® MACINTOSH® OS X, UNIX, Unix-like system distributions(e.g., Berkeley Software Distribution (BSD), FreeBSD, NetBSD, OpenBSD,etc.), Linux distributions (e.g., RED HAT®, UBUNTU®, KUBUNTU®, etc.),IBM® OS/2, MICROSOFT® WINDOWS® (XP®, Vista®/7/8, etc.), APPLE® IOS®,GOOGLE® ANDROID®, BLACKBERRY® OS, or the like. User interface 1017 mayfacilitate display, execution, interaction, manipulation, or operationof program components through textual or graphical facilities. Forexample, user interfaces 1017 may provide computer interaction interfaceelements on a display system operatively connected to the computersystem 1001, such as cursors, icons, check boxes, menus, scrollers,windows, widgets, etc. Graphical user interfaces (GUIs) may be employed,including, without limitation, APPLE® MACINTOSH® operating systems'AQUA® platform, IBM® OS/2®, MICROSOFT® WINDOWS® (e.g., AERO®, METRO®,etc.), UNIX X-WINDOWS, web interface libraries (e.g., ACTIVEX®, JAVA®,JAVASCRIPT®, AJAX®, HTML, ADOBE® FLASH®, etc.), or the like.

In some embodiments, the computer system 1001 may implement a webbrowser 1018 stored program component. The web browser 1018 may be ahypertext viewing application, such as MICROSOFT® INTERNET EXPLORER®,GOOGLE® CHROME®, MOZILLA® FIREFOX®, APPLE® SAFARI®, etc. Secure webbrowsing may be provided using HTTPS (secure hypertext transportprotocol), secure sockets layer (SSL), Transport Layer Security (TLS),etc. Web browsers may utilize facilities such as AJAX®, DHTML, ADOBE®FLASH®, JAVASCRIPT®, JAVA®, application programming interfaces (APIs),etc. In some embodiments, the computer system 1001 may implement a mailserver 1019 stored program component. The mail server 1019 may be anInternet mail server such as MICROSOFT® EXCHANGE®, or the like. The mailserver 1019 may utilize facilities such as ASP, ActiveX, ANSI C++/C #,MICROSOFT .NET® CGI scripts, JAVA®, JAVASCRIPT®, PERL®, PHP®, PYTHON®,WebObjects, etc. The mail server 1019 may utilize communicationprotocols such as internet message access protocol (IMAP), messagingapplication programming interface (MAPI), MICROSOFT® EXCHANGE®, postoffice protocol (POP), simple mail transfer protocol (SMTP), or thelike. In some embodiments, the computer system 1001 may implement a mailclient 1020 stored program component. The mail client 1020 may be a mailviewing application, such as APPLE MAIL®, MICROSOFT ENTOURAGE®,MICROSOFT OUTLOOK®, MOZILLA THUNDERBIRD®, etc.

In some embodiments, computer system 1001 may store user/applicationdata 1021, such as the data, variables, records, etc. (e.g., activatedneurons data, Characteristic Feature Directives (CFDs) data,differentiating neurons data, missing features data, new training data,and so forth) as described in this disclosure. Such databases may beimplemented as fault-tolerant, relational, scalable, secure databasessuch as ORACLE® OR SYBASE®. Alternatively, such databases may beimplemented using standardized data structures, such as an array, hash,linked list, struct, structured text file (e.g., XML), table, or asobject-oriented databases (e.g., using OBJECTSTORE®, POET®, ZOPE®,etc.). Such databases may be consolidated or distributed, sometimesamong the various computer systems discussed above in this disclosure.It is to be understood that the structure and operation of the anycomputer or database component may be combined, consolidated, ordistributed in any working combination.

As will be appreciated by those skilled in the art, the techniquesdescribed in the various embodiments discussed above are not routine, orconventional, or well understood in the art. The techniques discussedabove provide for providing user-specific explanations for an outputgenerated by an ANN model by extracting relevant features from thetraining image, segregating attributes to generate one or more userspecific groups, generating a feature table, generating explanationsword by word in a sequence based on the concatenated received data andthe current word generated by the system, and updating the explanationsbased on the received fine-tuning parameters.

In particular, the techniques discussed above provide selectiveexplanations based on the domain knowledge level of a user (i.e.user-specific explanations), thereby allowing the user to use theproposed system efficiently and for the purpose intended. Further, thetechniques can be extended to provide language specific explanations orprovide language translations, which would allow increased adaptabilityof the proposed techniques. By building a vocabulary along with modeldevelopment, the techniques provide for a time-efficient andenergy-efficient solution. The techniques take into considerationactivations of the neurons responsible for different attributes, so asto provide user-specific explanations. Further, attributes provided fromthe users (domain experts) are based on an overall or holisticperspective, rather than specific unmistakable signatures found incertain neurons of the model. For example, considering a scenario wherea cardiac image is fed to a classifier model (AI model), and a neuronrepresenting blockage in an artery is activated. Now, if an end-user isa cardiologist (i.e. domain expert), the explanation may be “88%blockage” based on the degree of activation of this neuron and otherneurons representing blockage. On the other hand, if the user is apatient, the explanation may be “issue in arteries” based on whether theneurons corresponding to malfunctioning arteries are activated.

Further, the techniques described above may be employed in any kind ofANN including, but not limited to, deep neural network (DNN) such asrecurrent neural network (RNN), convolutional neural network (CNN), orthe like. Moreover, the techniques may be easily deployed in anycloud-based servers for access and use as an ‘application as a service’by any computing device including mobile device. For example, the ANNimprovement engine may be implemented on a cloud-based server and usedfor improving performance of various ANN based mobile deviceapplications.

The specification has described method and system for providinguser-specific explanations for an output generated by an ANN model. Theillustrated steps are set out to explain the exemplary embodimentsshown, and it should be anticipated that ongoing technologicaldevelopment will change the manner in which particular functions areperformed. These examples are presented herein for purposes ofillustration, and not limitation. Further, the boundaries of thefunctional building blocks have been arbitrarily defined herein for theconvenience of the description. Alternative boundaries can be defined solong as the specified functions and relationships thereof areappropriately performed. Alternatives (including equivalents,extensions, variations, deviations, etc., of those described herein)will be apparent to persons skilled in the relevant art(s) based on theteachings contained herein. Such alternatives fall within the scope andspirit of the disclosed embodiments.

Furthermore, one or more computer-readable storage media may be utilizedin implementing embodiments consistent with the present disclosure. Acomputer-readable storage medium refers to any type of physical memoryon which information or data readable by a processor may be stored.Thus, a computer-readable storage medium may store instructions forexecution by one or more processors, including instructions for causingthe processor(s) to perform steps or stages consistent with theembodiments described herein. The term “computer-readable medium” shouldbe understood to include tangible items and exclude carrier waves andtransient signals, i.e., be non-transitory. Examples include randomaccess memory (RAM), read-only memory (ROM); volatile memory,nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, andany other known physical storage media.

It is intended that the disclosure and examples be considered asexemplary only, with a true scope and spirit of disclosed embodimentsbeing indicated by the following claim.

What is claimed is:
 1. A method of providing user-specific explanationsfor an output generated by an Artificial Neural Network (ANN) model, themethod comprising: receiving, by a vocabulary and explanation generatingdevice, a training dataset; identifying, by the vocabulary andexplanation generating device, one or more relevant features from thetraining dataset; distributing, by the vocabulary and explanationgenerating device, the one or more relevant features into a plurality ofgroups, wherein the plurality of groups correspond to a plurality oflevels of domain knowledge of users; and generating, by the vocabularyand explanation generating device, a plurality of vocabularies ofexplanations for an output generated by an ANN model for the trainingdataset corresponding to the plurality of groups, using the one or morerelevant features.
 2. The method of claim 1 further comprising:receiving a test dataset; receiving a target group signaturecorresponding to a target group from the plurality of groups; andselecting a vocabulary of explanation for the output generated by theANN model for the test dataset from the plurality of vocabularies ofexplanations, based on the target group signature.
 3. The method ofclaim 2, wherein the training dataset and the test dataset comprises atleast one of an image file and a video file.
 4. The method of claim 3,wherein generating the plurality of vocabularies of explanationscomprise receiving one or more captions corresponding to each of the oneor more relevant features, wherein the one or more captions aregenerated using Common Objects in Context (COCO), and wherein the one ormore attributes associated with each of the one or more relevantfeatures are generated from the one or more captions using NaturalLanguage Processing (NLP).
 5. The method of claim 1, wherein the one ormore relevant features comprise at least one of one or more objectswithin the training dataset, an interaction between two or more objectswithin the training dataset, an action associated with an object, or oneor more attributes associated with each of the one or more objects,wherein the one or more attributes comprise at least one of a color ofthe object, a shape of the object, a size of the object, a position ofthe object, or an interaction of the object with other objects andsurroundings.
 6. The method of claim 1 further comprising generating aplurality of explanations for the output generated by the ANN model forthe training dataset corresponding to the plurality of groups, using theplurality of vocabularies of explanations, wherein the plurality ofexplanations are generated from the plurality of vocabularies ofexplanations using Long Short Term Memory (LSTM) model.
 7. The method ofclaim 6 further comprising: receiving, from a user, a correction to anexplanation of the plurality of explanations, wherein the correction tothe explanation is based on at least one of: determining by the userthat a word in the explanation corresponding to a group of the pluralityof groups is from a vocabulary of explanation corresponding to thegroup, and determining by the user that a word in the explanationcorresponding to a group is from a correct vocabulary of explanationcorresponding to the group.
 8. The method of claim 1, wherein generatingthe plurality of vocabularies of explanations comprises: receiving adataset signature corresponding to the training dataset from the ANNmodel; receiving a group signature corresponding to each of theplurality of groups; receiving a feature table, wherein the featuretable comprises connecting words of a language; and generating avocabulary for a group of the plurality of groups based on the datasetsignature, the group signature, and the feature table.
 9. The method ofclaim 8, wherein generating the plurality of vocabularies ofexplanations further comprises feeding the one or more relevant featuresof the training dataset at an output of a penultimate layer of the ANNmodel.
 10. The method of claim 1 further comprising: upon distributingthe one or more relevant features into a plurality of groups, arrangingand storing the one or more relevant features in form of a hierarchicalgraph, wherein the hierarchical graph represents the plurality of levelsof domain knowledge of the users.
 11. The method of claim 1, wherein theone or more relevant features are identified from a plurality offeatures associated with the training dataset, and wherein each of theplurality of features correspond to a neuron activated in the ANN modelfor generating an output for the training dataset.
 12. The method ofclaim 11, wherein identifying further comprises selecting the one ormore relevant features from the plurality of features associated withthe training dataset, based on a predetermined threshold level ofactivation of the neuron corresponding to the each of the plurality offeatures.
 13. A system for providing user-specific explanations for anoutput generated by an ANN model, the system comprising: a vocabularyand explanation generating device comprising at least one processor anda computer-readable medium storing instructions that, when executed bythe at least one processor, cause the at least one processor to performoperations comprising: receiving a training dataset; identifying one ormore relevant features from the training dataset; distributing the oneor more relevant features into a plurality of groups, wherein theplurality of groups correspond to a plurality of levels of domainknowledge of users; and generating a plurality of vocabularies ofexplanations for an output generated by an ANN model for the trainingdataset corresponding to the plurality of groups, using the one or morerelevant features.
 14. The system of claim 13, wherein the operationsfurther comprise: receiving a test dataset; receiving a target groupsignature corresponding to a target group from the plurality of groups;and selecting a vocabulary of explanation for the output generated bythe ANN model for the test dataset from the plurality of vocabularies ofexplanations, based on the target group signature.
 15. The system ofclaim 13, wherein generating the plurality of vocabularies ofexplanations comprise: receiving one or more captions corresponding toeach of the one or more relevant features, wherein the one or morecaptions are generated using Common Objects in Context (COCO), andwherein one or more attributes associated with each of the one or morerelevant features are generated from the one or more captions usingNatural Language Processing (NLP); receiving a dataset signaturecorresponding to the training dataset from the ANN model; receiving agroup signature corresponding to each of the plurality of groups;receiving a feature table, wherein the feature table comprisesconnecting words of a language; and generating a vocabulary for a groupof the plurality of groups based on the dataset signature, the groupsignature, and the feature table.
 16. The system of claim 13, whereinthe operations further comprise generating a plurality of explanationsfor the output generated by the ANN model for the training datasetcorresponding to the plurality of groups, using the plurality ofvocabularies of explanations, wherein the plurality of explanations aregenerated from the plurality of vocabularies of explanations using LongShort Term Memory (LSTM) model.
 17. The system of claim 16, wherein theoperations further comprise: receiving, from a user, a correction to anexplanation of the plurality of explanations, wherein the correction tothe explanation is based on at least one of: determining by the userthat a word in the explanation corresponding to a group of the pluralityof groups is from a vocabulary of explanation corresponding to thegroup, and determining by the user that a word in the explanationcorresponding to a group is from a correct vocabulary of explanationcorresponding to the group.
 18. The system of claim 13, wherein theoperations further comprise: upon distributing the one or more relevantfeatures into a plurality of groups, arranging and storing the one ormore relevant features in form of a hierarchical graph, wherein thehierarchical graph represents the plurality of levels of domainknowledge of the users.
 19. The system of claim 13, wherein the one ormore relevant features are identified from a plurality of featuresassociated with the training dataset, and wherein each of the pluralityof features correspond to a neuron activated in the ANN model forgenerating an output for the training dataset, and wherein identifyingfurther comprises selecting the one or more relevant features from theplurality of features associated with the training dataset, based on apredetermined threshold level of activation of the neuron correspondingto the each of the plurality of features.
 20. A non-transitorycomputer-readable medium storing computer-executable instructions forproviding user-specific explanations for an output generated by anArtificial Neural Network (ANN) model, the computer-executableinstructions configured for: receiving a training dataset; identifyingone or more relevant features from the training dataset; distributingthe one or more relevant features into a plurality of groups, whereinthe plurality of groups corresponds to a plurality of levels of domainknowledge of users; and generating a plurality of vocabularies ofexplanations for an output generated by an ANN model for the trainingdataset corresponding to the plurality of groups, using the one or morerelevant features.